Databricks Databricks Certification DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-SCIENTIST Questions & Answers

• Question 121:

  

  May have a trend component that is quadratic in nature. Which pattern of data will indicate that the trend in the time series data is quadratic in nature?

  A. Naive Bayesian classifier

  B. Decision tree

  C. Linear regression

  D. K-means clustering

  Correct Answer: D

  Explanation: kmeans uses an iterative algorithm that minimizes the sum of distances from each object to its cluster centroid, over all clusters. This algorithm moves objects between clusters until the sum cannot be decreased further. The result is a set of clusters that are as compact and well-separated as possible. You can control the details of the minimization using several optional input parameters to kmeans, including ones for the initial values of the cluster centroids, and for the maximum number of iterations. Clustering is primarily an exploratory technique to discover hidden structures of the data, possibly as a prelude to more focused analysis or decision processes. Some specific applications of k-means are image processing, medical and customer segmentation. Clustering is often used as a lead-in to classification. Once the clusters are identified, labels can be applied to each cluster to classify each group based on its characteristics. Marketing and sales groups use k-means to better identify customers who have similar behaviors and spending patterns.

• Question 122:

  Select the correct option from the below:

  A. If you're trying to predict or forecast a target value^ then you need to look into supervised learning.

  B. If you've chosen supervised learning, with discrete target value like Yes/No. 1/2/3, A/B/C: or Red/Yellow/Black, then look into classification.

  C. If the target value can take on a number of values, say any value from 0.00 to 100.00, or -999 to 999: or +_to -_, then you need to look unsupervised learning

  D. If you're not trying to predict a target value, then you need to look into unsupervised learning

  E. Are you trying to fit your data into some discrete groups? If so and that's all you need, you should look into clustering.

  Correct Answer: ABDE

  Explanation: If you re trying to predict or forecast a target value, then you need to look into supervised learning. If not, then unsupervised learning is the place you want to be. If you've chosen supervised learning, what's your target value? Is it a discrete value like Yes/No, 1/2/3, A/B/C: or Red/Yellow/Black? If so, then you want to look into classification. If the target value can take on a number of values, say any value from 0.00 to 100.00, or-999 to 999, or+_to -_, then you need to look into regression. If you're not trying to predict a target value: then you need to look into unsupervised learning. Are you trying to fit your data into some discrete groups? If so and that's all you need, you should look into clustering. Do you need to have some numerical estimate of how strong the fit is into each group? If you answer yes then you probably should look into a density estimation algorithm.

• Question 123:

  Feature Hashing approach is "SGD-based classifiers avoid the need to predetermine vector size by simply picking a reasonable size and shoehorning the training data into vectors of that size" now with large vectors or with multiple locations per feature in Feature hashing?

  A. Is a problem with accuracy

  B. It is hard to understand what classifier is doing

  C. It is easy to understand what classifier is doing

  D. Is a problem with accuracy as well as hard to understand what classifier us doing

  Correct Answer: B

  Explanation: FEATURE HASHING SGD-based classifiers avoid the need to predetermine vector size by simply picking a reasonable size and shoehorning the training data into vectors of that size. This approach is known as feature hashing. The shoehorning is done by picking one or more locations by using a hash of the name of the variable for continuous variables or a hash of the variable name and the category name or word for categorical, text-like, or word-like data. This hashed feature approach has the distinct advantage of requiring less memory and one less pass through the training data, but it can make it much harder to reverse engineer vectors to determine which original feature mapped to a vector location. This is because multiple features may hash to the same location. With large vectors or with multiple locations per feature, this isn't a problem for accuracy but it can make it hard to understand what a classifier is doing. An additional benefit of feature hashing is that the unknown and unbounded vocabularies typical of word-like variables aren't a problem.

• Question 124:

  Scenario: Suppose that Bob can decide to go to work by one of three modes of transportation, car, bus, or commuter train. Because of high traffic, if he decides to go by car. there is a 50% chance he will be late. If he goes by bus, which has special reserved lanes but is sometimes overcrowded, the probability of being late is only 20%. The commuter train is almost never late, with a probability of only 1 %, but is more expensive than the bus.

  Suppose that Bob is late one day, and his boss wishes to estimate the probability that he drove to work that day by car. Since he does not know Which mode of transportation Bob usually uses, he gives a prior probability of 1 3 to each of the three possibilities. Which of the following method the boss will use to estimate of the probability that Bob drove to work?

  A. Naive Bayes

  B. Linear regression

  C. Random decision forests

  D. None of the above

  Correct Answer: A

  Explanation: Bayes' theorem (also known as Bayes' rule) is a useful tool for calculating conditional probabilities.

• Question 125:

  What type of output generated in case of linear regression?

  A. Continuous variable

  B. Discrete Variable

  C. Any of the Continuous and Discrete variable

  D. Values between 0 and 1

  Correct Answer: A

  Explanation: Linear regression model generate continuous output variable.

• Question 126:

  You are studying the behavior of a population, and you are provided with multidimensional data at the individual level. You have identified four specific individuals who are valuable to your study, and would like to find all users who are most similar to each individual. Which algorithm is the most appropriate for this study?

  A. Association rules

  B. Decision trees

  C. Linear regression

  D. K-means clustering

  Correct Answer: D

  Explanation: kmeans uses an iterative algorithm that minimizes the sum of distances from each object to its cluster centroid, over all clusters. This algorithm moves objects between clusters until the sum cannot be decreased further. The result is a set of clusters that are as compact and well-separated as possible. You can control the details of the minimization using several optional input parameters to kmeans, including ones for the initial values of the cluster centroids, and for the maximum number of iterations. Clustering is primarily an exploratory technique to discover hidden structures of the data: possibly as a prelude to more focused analysis or decision processes. Some specific applications of k-means are image processing^ medical and customer segmentation. Clustering is often used as a lead-in to classification. Once the clusters are identified, labels can be applied to each cluster to classify each group based on its characteristics. Marketing and sales groups use k-means to better identify customers who have similar behaviors and spending patterns.

• Question 127:

  You are having 1000 patients' data with the height and age. Where age in years and height in meters. You wanted to create cluster using this two attributes. You wanted to have near equal effect for both the age and height while creating the cluster. What you can do?

  A. You will be adding height with the numeric value 100

  B. You will be converting each height value to centimeters

  C. You will be dividing both age and height with their respective standard deviation

  D. You will be taking square root of height

  Correct Answer: BC

  Explanation: When you see the data age in years would have values like 50, 60r 70 90 years etc. And while calculating distance from centroid maximum possible value can be 90- 0 and its square will be 8100.

  While using heights in meter can be 2-0.5(1.5) meters and its square will be 2.25 only. So you can see age has more effect than height. Hence bringing the height on same level you can convert it into centimeters. Can bring data upto 200

  centimeters and then it be more effective like square of 200 maximum.

  However there is another approach is to divide the each value with its standard deviation, which will not have impact of the units e.g. age/sd of the age, which results in value without unit. This can also help in reducing the effect of units.

• Question 128:

  Which of the following statement true with regards to Linear Regression Model?

  A. Ordinary Least Square can be used to estimates the parameters in linear model

  B. In Linear model, it tries to find multiple lines which can approximate the relationship between the outcome and input variables.

  C. Ordinary Least Square is a sum of the individual distance between each point and the fitted line of regression model.

  D. Ordinary Least Square is a sum of the squared individual distance between each point and the fitted line of regression model.

  Correct Answer: AD

  Explanation: Linear regression model are represented using the below equation

  

  Where B(0) is intercept and B(1) is a slope. As B(0) and B(1) changes then fitted line also shifts accordingly on the plot. The purpose of the Ordinary Least Square method is to estimates these parameters B(0) and B(1). And similarly it is a sum of squared distance between the observed point and the fitted line. Ordinary least squares (OLS) regression minimizes the sum of the squared residuals. A model fits the data well if the differences between the observed values and the model's predicted values are small and unbiased.

• Question 129:

  Spam filtering of the emails is an example of

  A. Supervised learning

  B. Unsupervised learning

  C. Clustering

  D. 1 and 3 are correct

  E. 2 and 3 are correct

  Correct Answer: A

  Explanation: Clustering is an example of unsupervised learning. The clustering algorithm finds groups within the data without being told what to look for upfront. This contrasts with classification, an example of supervised machine learning, which is the process of determining to which class an observation belongs. A common application of classification is spam filtering. With spam filtering we use labeled data to train the classifier: e-mails marked as spam or ham.

• Question 130:

  A fruit may be considered to be an apple if it is red, round, and about 3" in diameter. A naive Bayes classifier considers each of these features to contribute independently to the probability that this fruit is an apple, regardless of the:

  A. Presence of the other features.

  B. Absence of the other features.

  C. Presence or absence of the other features

  D. None of the above

  Correct Answer: C

  Explanation: In simple terms, a naive Bayes classifier assumes that the value of a particular feature is unrelated to the presence or absence of any other feature, given the class variable. For example, a fruit may be considered to be an apple if it is red, round, and about 3" in diameter A naive Bayes classifier considers each of these features to contribute independently to the probability that this fruit is an apple, regardless of the presence or absence of the other features.